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              <p class="caption"><span class="caption-text">GETTING STARTED</span></p>
<ul class="current">
<li class="toctree-l1"><a class="reference internal" href="intro.html">Introduction</a></li>
<li class="toctree-l1 current"><a class="current reference internal" href="#">Openspeech’s Hydra configuration</a><ul>
<li class="toctree-l2"><a class="reference internal" href="#what-is-hydra">What is Hydra?</a></li>
<li class="toctree-l2"><a class="reference internal" href="#motivation">Motivation</a></li>
<li class="toctree-l2"><a class="reference internal" href="#creating-or-migrating-components">Creating or migrating components</a><ul>
<li class="toctree-l3"><a class="reference internal" href="#example">Example:</a></li>
<li class="toctree-l3"><a class="reference internal" href="#register-function"><code class="docutils literal notranslate"><span class="pre">&#64;register_*()</span></code> function</a><ul>
<li class="toctree-l4"><a class="reference internal" href="#model-example">Model example:</a></li>
<li class="toctree-l4"><a class="reference internal" href="#dataset-example">Dataset example:</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l2"><a class="reference internal" href="#openspeech-s-configuration-structure">Openspeech’s configuration structure</a></li>
<li class="toctree-l2"><a class="reference internal" href="#training-with-hydra-train-py">Training with <code class="docutils literal notranslate"><span class="pre">hydra_train.py</span></code></a><ul>
<li class="toctree-l3"><a class="reference internal" href="#override-default-values-through-command-line">1. Override default values through command line:</a></li>
<li class="toctree-l3"><a class="reference internal" href="#add-new-configuration-through-command-line">2. Add new configuration through command line:</a></li>
</ul>
</li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="configs.html">Openspeech’s configurations</a></li>
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<p class="caption"><span class="caption-text">OPENSPEECH MODELS</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../models/Openspeech Model.html">Openspeech Model</a></li>
<li class="toctree-l1"><a class="reference internal" href="../models/Openspeech CTC Model.html">Openspeech CTC Model</a></li>
<li class="toctree-l1"><a class="reference internal" href="../models/Openspeech Encoder Decoder Model.html">Openspeech Encoder Decoder Model</a></li>
<li class="toctree-l1"><a class="reference internal" href="../models/Openspeech Transducer Model.html">Openspeech Transducer Model</a></li>
<li class="toctree-l1"><a class="reference internal" href="../models/Openspeech Language Model.html">Openspeech Language Model</a></li>
</ul>
<p class="caption"><span class="caption-text">MODEL ARCHITECTURES</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../architectures/Conformer.html">Conformer</a></li>
<li class="toctree-l1"><a class="reference internal" href="../architectures/ContextNet.html">ContextNet</a></li>
<li class="toctree-l1"><a class="reference internal" href="../architectures/DeepSpeech2.html">DeepSpeech2</a></li>
<li class="toctree-l1"><a class="reference internal" href="../architectures/Jasper.html">Jasper</a></li>
<li class="toctree-l1"><a class="reference internal" href="../architectures/Listen Attend Spell.html">Listen Attend Spell Model</a></li>
<li class="toctree-l1"><a class="reference internal" href="../architectures/LSTM LM.html">LSTM Language Model</a></li>
<li class="toctree-l1"><a class="reference internal" href="../architectures/QuartzNet.html">QuartzNet Model</a></li>
<li class="toctree-l1"><a class="reference internal" href="../architectures/RNN Transducer.html">RNN Transducer Model</a></li>
<li class="toctree-l1"><a class="reference internal" href="../architectures/Transformer.html">Transformer Model</a></li>
<li class="toctree-l1"><a class="reference internal" href="../architectures/Transformer LM.html">Transformer Language Model</a></li>
<li class="toctree-l1"><a class="reference internal" href="../architectures/Transformer Transducer.html">Transformer Transducer Model</a></li>
</ul>
<p class="caption"><span class="caption-text">CORPUS</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../corpus/AISHELL-1.html">AISHELL</a></li>
<li class="toctree-l1"><a class="reference internal" href="../corpus/KsponSpeech.html">KsponSpeech</a></li>
<li class="toctree-l1"><a class="reference internal" href="../corpus/LibriSpeech.html">LibriSpeech</a></li>
</ul>
<p class="caption"><span class="caption-text">LIBRARY REFERENCE</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="../modules/Callback.html">Callback</a></li>
<li class="toctree-l1"><a class="reference internal" href="../modules/Criterion.html">Criterion</a></li>
<li class="toctree-l1"><a class="reference internal" href="../modules/Data Augment.html">Data Augment</a></li>
<li class="toctree-l1"><a class="reference internal" href="../modules/Feature Transform.html">Feature Transform</a></li>
<li class="toctree-l1"><a class="reference internal" href="../modules/Datasets.html">Datasets</a></li>
<li class="toctree-l1"><a class="reference internal" href="../modules/Data Loaders.html">Data Loaders</a></li>
<li class="toctree-l1"><a class="reference internal" href="../modules/Decoders.html">Decoders</a></li>
<li class="toctree-l1"><a class="reference internal" href="../modules/Encoders.html">Encoders</a></li>
<li class="toctree-l1"><a class="reference internal" href="../modules/Modules.html">Modules</a></li>
<li class="toctree-l1"><a class="reference internal" href="../modules/Optim.html">Optim</a></li>
<li class="toctree-l1"><a class="reference internal" href="../modules/Search.html">Search</a></li>
<li class="toctree-l1"><a class="reference internal" href="../modules/Tokenizers.html">Tokenizers</a></li>
<li class="toctree-l1"><a class="reference internal" href="../modules/Metric.html">Metric</a></li>
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  <div class="section" id="openspeech-s-hydra-configuration">
<h1>Openspeech’s Hydra configuration<a class="headerlink" href="#openspeech-s-hydra-configuration" title="Permalink to this headline">¶</a></h1>
<p>This page describes how openspeech uses <a class="reference external" href="https://github.com/facebookresearch/hydra">Hydra</a> to manage configuration.</p>
<div class="section" id="what-is-hydra">
<h2>What is Hydra?<a class="headerlink" href="#what-is-hydra" title="Permalink to this headline">¶</a></h2>
<p><a class="reference external" href="https://github.com/facebookresearch/hydra">Hydra</a> is an open-source Python framework that simplifies the development of research and other complex applications. The key feature is the ability to dynamically create a hierarchical configuration by composition and override it through config files and the command line. The name Hydra comes from its ability to run multiple similar jobs - much like a Hydra with multiple heads.</p>
</div>
<div class="section" id="motivation">
<h2>Motivation<a class="headerlink" href="#motivation" title="Permalink to this headline">¶</a></h2>
<p>Openspeech aims to provide as many options as possible. However, too many options cause a lot of confusion to users.
. To address this problem, we needed a hierarchical configuration management toolkit.
Hydra was the best choice for us in that respect. We have referred to the structure of <a class="reference external" href="https://github.com/pytorch/fairseq">Fairseq</a> that successfully applied Hydra.
Thank you for fairseq team.</p>
<p>We thought a lot about which method is better, using the <code class="docutils literal notranslate"><span class="pre">YAML</span></code> file or using <code class="docutils literal notranslate"><span class="pre">&#64;dataclass</span></code>.
After much consideration, we decided to use <code class="docutils literal notranslate"><span class="pre">&#64;dataclass</span></code>, which is easy to understand each module’s configuration.
<code class="docutils literal notranslate"><span class="pre">&#64;dataclass</span></code> has default values for that module and has been configured to be stored in the same Python file as each module.</p>
<p>Additionally, Hydra has a rich and growing library of plugins that provide functionality such as hyperparameter sweeping (including using bayesian optimization through the Ax library), job launching across various platforms, and more.</p>
</div>
<div class="section" id="creating-or-migrating-components">
<h2>Creating or migrating components<a class="headerlink" href="#creating-or-migrating-components" title="Permalink to this headline">¶</a></h2>
<p>In general, each new (or updated) component should provide a companion <a class="reference external" href="https://www.python.org/dev/peps/pep-0557/">dataclass</a>.
These dataclass are typically located in the same file as the component and are passed as arguments to the <code class="docutils literal notranslate"><span class="pre">register_*()</span></code> functions.
These classes are decorated with a <code class="docutils literal notranslate"><span class="pre">&#64;dataclass</span></code> decorator, and typically inherit from <code class="docutils literal notranslate"><span class="pre">OpenspeechDataclass</span></code>.</p>
<div class="section" id="example">
<h3>Example:<a class="headerlink" href="#example" title="Permalink to this headline">¶</a></h3>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">dataclasses</span> <span class="kn">import</span> <span class="n">dataclass</span><span class="p">,</span> <span class="n">field</span>
<span class="kn">from</span> <span class="nn">openspeech.dataclass.configurations</span> <span class="kn">import</span> <span class="n">OpenspeechDataclass</span>


<span class="nd">@dataclass</span>
<span class="k">class</span> <span class="nc">ConformerLSTMConfigs</span><span class="p">(</span><span class="n">OpenspeechDataclass</span><span class="p">):</span>
    <span class="n">model_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span>
        <span class="n">default</span><span class="o">=</span><span class="s2">&quot;conformer_lstm&quot;</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Model name&quot;</span><span class="p">}</span>
    <span class="p">)</span>
    <span class="n">encoder_dim</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span>
        <span class="n">default</span><span class="o">=</span><span class="mi">256</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Dimension of encoder.&quot;</span><span class="p">}</span>
    <span class="p">)</span>
</pre></div>
</div>
</div>
<div class="section" id="register-function">
<h3><code class="docutils literal notranslate"><span class="pre">&#64;register_*()</span></code> function<a class="headerlink" href="#register-function" title="Permalink to this headline">¶</a></h3>
<p>We actively utilized the <code class="docutils literal notranslate"><span class="pre">&#64;register_*()</span></code> function inspired by <a class="reference external" href="https://github.com/pytorch/fairseq">Fairseq</a>.
The function <code class="docutils literal notranslate"><span class="pre">&#64;register_*()</span></code> automatically registers classes and associated data classes.
This method is very effective when adding new modules.
Below is an example of how <code class="docutils literal notranslate"><span class="pre">register_*()</span></code> functions and data classes are utilized in <code class="docutils literal notranslate"><span class="pre">Openspeech</span></code>.</p>
<div class="section" id="model-example">
<h4>Model example:<a class="headerlink" href="#model-example" title="Permalink to this headline">¶</a></h4>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@dataclass</span>
<span class="k">class</span> <span class="nc">TransformerConfigs</span><span class="p">(</span><span class="n">ModelConfigs</span><span class="p">):</span>
    <span class="n">model_name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span>
        <span class="n">default</span><span class="o">=</span><span class="s2">&quot;transformer&quot;</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Model name&quot;</span><span class="p">}</span>
    <span class="p">)</span>
    <span class="n">extractor</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span>
        <span class="n">default</span><span class="o">=</span><span class="s2">&quot;vgg&quot;</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;The CNN feature extractor.&quot;</span><span class="p">}</span>
    <span class="p">)</span>
    <span class="o">...</span>


<span class="nd">@register_model</span><span class="p">(</span><span class="s1">&#39;transformer&#39;</span><span class="p">,</span> <span class="n">dataclass</span><span class="o">=</span><span class="n">TransformerConfigs</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">SpeechTransformerModel</span><span class="p">(</span><span class="n">OpenspeechEncoderDecoderModel</span><span class="p">):</span>
    <span class="o">...</span>
    <span class="k">def</span> <span class="nf">build_model</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="o">...</span>
</pre></div>
</div>
</div>
<div class="section" id="dataset-example">
<h4>Dataset example:<a class="headerlink" href="#dataset-example" title="Permalink to this headline">¶</a></h4>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="nd">@dataclass</span>
<span class="k">class</span> <span class="nc">MelSpectrogramConfigs</span><span class="p">(</span><span class="n">AudioConfigs</span><span class="p">):</span>
    <span class="n">name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span>
        <span class="n">default</span><span class="o">=</span><span class="s2">&quot;melspectrogram&quot;</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;Name of dataset.&quot;</span><span class="p">}</span>
    <span class="p">)</span>
    <span class="n">num_mels</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="n">field</span><span class="p">(</span>
        <span class="n">default</span><span class="o">=</span><span class="mi">80</span><span class="p">,</span> <span class="n">metadata</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;help&quot;</span><span class="p">:</span> <span class="s2">&quot;The number of mfc coefficients to retain.&quot;</span><span class="p">}</span>
    <span class="p">)</span>
    <span class="o">...</span>


<span class="nd">@register_dataset</span><span class="p">(</span><span class="s2">&quot;melspectrogram&quot;</span><span class="p">,</span> <span class="n">dataclass</span><span class="o">=</span><span class="n">MelSpectrogramConfigs</span><span class="p">)</span>
<span class="k">class</span> <span class="nc">MelSpectrogramDataset</span><span class="p">(</span><span class="n">AudioDataset</span><span class="p">):</span>
    <span class="o">...</span>
    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
        <span class="o">...</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="section" id="openspeech-s-configuration-structure">
<h2>Openspeech’s configuration structure<a class="headerlink" href="#openspeech-s-configuration-structure" title="Permalink to this headline">¶</a></h2>
<p>Below are the configuration dataclasses that you can select from <code class="docutils literal notranslate"><span class="pre">Openspeech</span></code>.</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">defaults</span><span class="p">:</span>
  <span class="o">-</span> <span class="n">audio</span><span class="p">:</span> 
    <span class="o">-</span> <span class="n">fbank</span>
    <span class="o">-</span> <span class="n">melspectrogram</span>
    <span class="o">-</span> <span class="n">mfcc</span>
    <span class="o">-</span> <span class="n">spectrogram</span>
  <span class="o">-</span> <span class="n">common</span><span class="p">:</span> 
    <span class="o">-</span> <span class="n">kspon</span>
    <span class="o">-</span> <span class="n">libri</span>
    <span class="o">-</span> <span class="n">aishell</span>
  <span class="o">-</span> <span class="n">criterion</span><span class="p">:</span> 
    <span class="o">-</span> <span class="n">cross_entropy</span>
    <span class="o">-</span> <span class="n">ctc</span>
    <span class="o">-</span> <span class="n">joint_ctc_cross_entropy</span>
    <span class="o">-</span> <span class="n">label_smoothed_cross_entropy</span>
    <span class="o">-</span> <span class="n">transducer</span>
  <span class="o">-</span> <span class="n">lr_scheduler</span><span class="p">:</span> 
    <span class="o">-</span> <span class="n">reduce_lr_on_plateau</span>
    <span class="o">-</span> <span class="n">transformer</span>
    <span class="o">-</span> <span class="n">tri_stage</span>
    <span class="o">-</span> <span class="n">warmup_reduce_lr_on_plateau</span>
    <span class="o">-</span> <span class="n">warmup</span>
  <span class="o">-</span> <span class="n">model</span><span class="p">:</span> 
    <span class="o">-</span> <span class="n">conformer_encoder_only</span>
    <span class="o">-</span> <span class="n">conformer_lstm</span>
    <span class="o">-</span> <span class="n">conformer_transducer</span>
    <span class="o">-</span> <span class="n">deepspeech2</span>
    <span class="o">-</span> <span class="n">jasper</span>
    <span class="o">-</span> <span class="n">listen_attend_spell</span>
    <span class="o">-</span> <span class="n">rnn_transducer</span>
    <span class="o">-</span> <span class="n">transformer</span>
    <span class="o">-</span> <span class="n">transformer_transducer</span>
  <span class="o">-</span> <span class="n">trainer</span><span class="p">:</span> 
    <span class="o">-</span> <span class="n">cpu</span>
    <span class="o">-</span> <span class="n">gpu</span>
    <span class="o">-</span> <span class="n">tpu</span>
    <span class="o">-</span> <span class="n">cpu</span><span class="o">-</span><span class="n">fp64</span>
    <span class="o">-</span> <span class="n">gpu</span><span class="o">-</span><span class="n">fp16</span>
    <span class="o">-</span> <span class="n">tpu</span><span class="o">-</span><span class="n">fp16</span>
  <span class="o">-</span> <span class="n">vocab</span><span class="p">:</span> 
    <span class="o">-</span> <span class="n">aishell_character</span>
    <span class="o">-</span> <span class="n">kspon_character</span>
    <span class="o">-</span> <span class="n">kspon_subword</span>
    <span class="o">-</span> <span class="n">kspon_grapheme</span>
    <span class="o">-</span> <span class="n">libri_character</span>
    <span class="o">-</span> <span class="n">libri_subword</span>
</pre></div>
</div>
</div>
<div class="section" id="training-with-hydra-train-py">
<h2>Training with <code class="docutils literal notranslate"><span class="pre">hydra_train.py</span></code><a class="headerlink" href="#training-with-hydra-train-py" title="Permalink to this headline">¶</a></h2>
<p>On startup, Hydra will create a configuration object that contains a hierarchy of all the necessary dataclasses populated with their default values in the code.</p>
<p>Some of the most common use cases are shown below:</p>
<div class="section" id="override-default-values-through-command-line">
<h3>1. Override default values through command line:<a class="headerlink" href="#override-default-values-through-command-line" title="Permalink to this headline">¶</a></h3>
<div class="highlight-diff notranslate"><div class="highlight"><pre><span></span>$ python ./openspeech_cli/hydra_train.py \
    common=libri \
<span class="gi">+   common.dataset_path=$DATASET_PATH \</span>
<span class="gi">+   common.dataset_download=True \</span>
<span class="gi">+   common.manifest_file_path=$MANIFEST_FILE_PATH \  </span>
    vocab=libri_subword \
<span class="gi">+   vocab.vocab_size=10000 \</span>
    model=conformer_lstm \
<span class="gi">+   model.encoder_dim=320 \</span>
    audio=mfcc \
    lr_scheduler=warmup_reduce_lr_on_plateau \
    trainer=gpu-fp16 \
    criterion=ctc
</pre></div>
</div>
<p>Note that along with explicitly providing values for parameters such as <code class="docutils literal notranslate"><span class="pre">common.dataset_path</span></code>, this also tells Hydra to overlay configuration found in dataclass.
If you want to train a model without specifying a particular architecture you can simply specify <code class="docutils literal notranslate"><span class="pre">model=conformer_lstm</span></code>.</p>
</div>
<div class="section" id="add-new-configuration-through-command-line">
<h3>2. Add new configuration through command line:<a class="headerlink" href="#add-new-configuration-through-command-line" title="Permalink to this headline">¶</a></h3>
<div class="highlight-diff notranslate"><div class="highlight"><pre><span></span>$ python ./openspeech_cli/hydra_train.py \
    common=libri \
    vocab=libri_subword \
    model=conformer_lstm \
    audio=mfcc \
    lr_scheduler=warmup_reduce_lr_on_plateau \
    trainer=gpu-fp16 \
<span class="gi">+   +trainer.is_gpu=True \</span>
<span class="gi">+   +trainer.is_tpu=False \</span>
    criterion=ctc \
</pre></div>
</div>
<p>More detailed methods of using hydra can be found <a class="reference external" href="https://hydra.cc/">Hydra website</a>. If you have any questions, feel free to send me an email or create an issue.</p>
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